VEGETATION PREPARATORY PROGRAMME
PRELAUNCH PHASE
FINAL REPORT
June 15, 1998
IMPROVED ATMOSPHERIC CORRECTIONS AND
DATA COMPOSITING METHODS FOR SURFACE
REFLECTANCE RETRIEVAL
Principal Investigator :
Marc LEROY
CESBIO
Toulouse
CoInvestigators :
Gérard DEDIEU
Jean-Louis ROUJEAN
CESBIO
CNRM
Toulouse
Toulouse
Participants
Béatrice BERTHELOT
Olivier HAUTECOEUR
Patrice BICHERON
Roselyne LACAZE
CESBIO
CESBIO
CESBIO
CNRM
Toulouse
Toulouse
Toulouse
Toulouse
CONTENTS
Executive Summary
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1 . Introduction
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2. Data simulation plan
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2.1 Satellite data
2.2 Airborne data
2.3 Sun photometer network
2.4 Miscellaneous
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3. Atmospheric corrections and cloud screening
3.1 SMAC upgrading
3.2 Water vapour
3.3 Ozone
3.4 Aerosols
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3.4.1 Sensitivity of blue band to aerosol optical thickness
3.4.2 Principles of the proposed algorithm
3.4.3 Results
3.4.4 Discussion and conclusion
3.5 Cloud screening
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4. Correction for angular effects
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4.1 Position of the problem
4.2 Test of compositing methods using AVHRR data on selected sites …….
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4.2.1 Issues of interest
4.2.2 Test of different BRDF models
4.2.3 Comparison of AVHRR/airborne POLDER BRDFs
4.2.4 Output variables
4.2.5 Discussion
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6. Future work
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7. References
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4.3 Perturbating effects
4.3.1 Temporal variabilities of surface reflectances
4.3.2 Coupling of surface and atmosphere effects in BRDF retrieval
5. Recommendations for VGT
5.1 Atmospheric corrections
5.2 Cloud filtering
5.3 Correction for angular effects
5.4 Algorithmic outline
2
41
Executive Summary
This investigation aims at an improvement of the accuracy of surface reflectance products delivered
by the VEGETATION system. The foreseen improvement of products concerns the algorithms of
atmospheric corrections, cloud screening and removal of anisotropy effects.
The first step of this study has been to gather a data set allowing to address the various issues. For
atmospheric corrections, ECMWF and climatologies of ozone or water vapor concentrations have
been acquired. POLDER/ADEOS data on selected sites equipped with sunphotometer data have been
processed to test aerosol correction methods. Cloud filtering techniques are discussed on the basis of
the LASUR product, processed from several years of global AVHRR/GVI data. A data set of seasonal
full resolution AVHRR data, together with several datasets of airborne POLDER data, have been
acquired on 3 test sites (HAPEX-Sahel site in Summer-Autumn 1992, BOREAS site in SpringSummer 1994, Alpilles site in Spring 1996), for the purpose of testing methods of correction and
normalization of angular effects.
Atmospheric corrections: Inaccuracies of atmospheric correction procedures lead to significant errors
on surface reflectances retrieved from the sensor measurements. The nominal VEGETATION
products are corrected for atmospheric effects using the SMAC code (Rahman and Dedieu, 1994), in
which the water vapor, ozone and aerosol contents are given by a climatology or by some prescribed
value. It is clear that any error on these contents translates into a corresponding error on retrieved
surface reflectances. In this report, we first seek to improve the accuracy of these atmospheric
contents. One of the issues concerns the choice of sources for the water vapor and ozone
concentrations. We have compared simulated TOA (Top Of the Atmosphere) reflectances using as
input climatologic or ECMWF data for water vapor, and using as input climatologic or TOVS/NOAA
data for ozone content. The conclusion is that while a climatology is sufficient for ozone, daily water
vapor contents derived from meteorological models have to be used.
So far, a crude correction for aerosol effects is made with VEGETATION data, using fixed aerosol
amounts per band of latitude. We propose here an original method of retrieval of aerosol content
based mainly on the use of the B0 VEGETATION blue channel on a monthly period of data
acquisition preceding the current day of observation. This method is partially validated with radiative
transfer simulations of TOA reflectances and with time series of POLDER/ADEOS data on sites
equipped with AERONET sunphotometer measurements.
Cloud screening is generally difficult with coarse resolution sensors, particularly if they are not
equipped with thermal channels. Based on some cloud screening work made on the LASUR dataset,
and also with experience gained using POLDER/ADEOS data, we propose a method based on
thresholding the B0 and MIR channels on daily data, followed by a thresholding of temporal profiles
of vegetation indices and B0 channels over a monthly period.
Anisotropy removal : The nominal VEGETATION products use the Maximum Value Composite
(MVC) technique as a compositing method of temporal series of data. This compositing tends to
attenuate undesirable effects due to directional effects (and also cloud contamination of pixels, and
atmospheric effects). The basic idea developed here is to remove the directional effects with a BRDF
estimated self consistently with a time series of VEGETATION data. The problem is analyzed with
time series of AVHRR data on 3 selected sites characteristic of very different biomes and on which
field campaigns have been made : semi-arid (HAPEX 92), boreal forest (BOREAS 94), and
agricultural temperate site (Alpilles 96). Three BRDF models adjusted against the data are
intercompared on the basis of their ability to provide smooth temporal profiles of normalized
reflectances, and to reconstruct BRDFs as close as possible from reference BRDFs measured with the
3
airborne POLDER instrument on each of the 3 sites. It is concluded that Walthall’s model satisfies
better the first criterion, and Roujean’s model the second. It is recommended that the ouput variables
produced by the operational processing center be spectral hemispherical reflectances. A period of
composition of 30 days is also recommended.
Such a compositing technique is meaningful only if the time variability of reflectances is sufficiently
weak. This is analysed with airborne POLDER data acquired on HAPEX and BOREAS, with the
conclusion that no significant time variability has been observed so far, provided the sun-view
configurations are rigorously the same in successive observations.
The atmosphere-surface coupling effect in the atmospheric correction arises from the fact that the
surface is usually assumed Lambertian in the correction. Simulation studies reported here show that
this effect is significant and should be corrected, for example with the same algorithm as that applied
to the MODIS instrument.
An algorithmic outline is provided at the end of the report. The main recommendation is that the
basic improvement of atmospheric corrections and data compositing should rely on the
temporal dimension of VEGETATION data. A 30-day temporal depth should be adequate for the
issues of cloud screening, aerosol correction, and removal of directional effects. It is also pointed out
that the full time resolution should be conserved, and not degraded as in usual compositing
techniques.
4
1. INTRODUCTION
This proposal aims at an improvement of the accuracy of the products of surface reflectance delivered by the
VEGETATION system. It is in fact well known that the accuracy of the surface reflectances retrieved from the
sensor measurements is limited by a series of perturbing factors, which include: (i) sensor calibration accuracy,
(ii) efficiency of cloud detection in pixels of coarse spatial resolution sensors, (iii) atmospheric correction
accuracy, and (iv) variations of sun and view angle configurations between successive measurements. Severe
errors in the assessment of vegetation phenology and land cover can result if these perturbing factors are
unsufficiently controlled. The accuracy of atmospheric corrections is generally poor essentially because space
and time distributions of atmospheric components are poorly documented, in particular the aerosols distribution.
On the other hand, the application of some temporal compositing method on the data acquired over a given
period of time, generally from 1 week to 1 month (called here the compositing period), permits to reduce the
effects of some of these perturbations. The most well known compositing method, which has been widely applied
with AVHRR data, has been so far the so-called Maximum Value Composite method (Tarpley et al., 1984), the
principle of which is to select the sensor data which maximizes the Normalized Vegetation Index (NDVI) in a
given compositing period. However, it is clear that other possible methodologies of compositing can be designed,
with a multiplicity of possible options (for example, use of Top Of the Atmosphere (TOA) reflectances or
atmospherically corrected reflectances, data filtering, duration of the compositing period, account or not of the
angular signature of the surface + atmosphere system, etc).
The present study has the objective of proposing improvements of the atmospheric corrections (including cloud
screening) and data compositing methods for the derivation of accurate surface reflectances, to be implemented
in a « second generation » operational, real time, processing center of VEGETATION data.
The first step of the study has been to gather a data set on which algorithmic studies are completed : spaceborne
AVHRR/NOAA and POLDER/ADEOS data on selected sites, airborne POLDER data on selected sites, global
vegetation indices GVI, AERONET data, global climatology and ECMWF meteorological data. The data plan is
described in Section 2.
The basic atmospheric correction tool is the SMAC code (Rahman et al., 1994), which has been updated to match
the recent 6S developments (Section 3.1). One of the problems concerns the choice of sources for the water
vapor and ozone contents. To make an appropriate choice we compare simulated TOA reflectances using as input
climatologic or ECMWF data for water vapor, and also using as input climatologic or TOVS/NOAA data for
ozone content (Section 3.2 and 3.3). The correction of the effects of the aerosols is traditionnally difficult to do.
We propose here an original correction method based mainly on the use of the B0 VEGETATION blue channel,
and validate this method with radiative transfer simulations of TOA reflectances and with time series of
POLDER/ADEOS data on sites equipped with AERONET sunphotometer measurements (Section 3.4). Cloud
screening is generally difficult with coarse resolution sensors, particularly if they are not equipped with thermal
channels. Based on some cloud screening work made on the LASUR dataset, we propose a method based on
thresholding temporal profiles of vegetation indices and possibly B0 (Section 3.5).
The removal of directional effects on time series of reflectances is a problem addressed by most modern sensors
(Section 4.1). The basic idea developed here is to remove the directional effects with a BRDF estimated self
consistently with a time series of VEGETATION data. The problem is analyzed with time series of AVHRR data
on 3 selected sites characteristic of very different biomes and on which field campaigns have been made : semiarid (HAPEX 92), boreal forest (BOREAS 94), and agricultural temperate site (Alpilles 96) (Section 4.2). Three
BRDF models adjusted against the data are intercompared on the basis of their ability to provide smooth
temporal profiles of normalized reflectances, and to reconstruct BRDFs as close as possible from reference
BRDFs measured with the airborne POLDER instrument on each of the 3 sites. Two extra sources of
perturbations are analyzed in Section 4.3 : the temporal variability of reflectances during the compositing period,
and the errors made by assuming the surface Lambertian when performing the atmospheric corrections.
On the basis of these various studies, we recommend in Section 5 specific procedures and algorithms of
atmospheric corrections, cloud screening and anisotropy removal.
5
The various tasks and associated planning of this investigation are summarized in Table 1, which has the same
structure as that given in the original proposal. Table 1 identifies also the text Sections associated to each Task to
facilitate the evaluation of project progress by the VEGETATION Scientific Comittee.
In the post-launch phase, the algorithms and procedures recommended in the pre-launch phase will be tested with
VEGETATION data on some selected sites in temperate and semi-arid regions on which atmospheric data from
the sunphotometer network PHOTONS can be collected. We will also evaluate during this phase the possibility
of operational use of atmospheric products delivered by the spaceborne POLDER/ADEOS system, such as water
vapor concentration, and aerosol optical thickness and phase function in the VEGETATION Ground Processing
Segment. This future work is summarized in Section 7.
Data simulation
plan
June 95 - September 96
Sept. 96 – Apr. 98
Task 1: Preparation of data relevant to the program,
including
- GVI data
- AVHRR data (Hapex 92; Alpilles 96; BOREAS 94)
- POLDER data (Hapex 92; Alpilles 96; BOREAS 94)
- ECMWF data
(see § 2)
Task 2 : comparison H2O
from ECMWF and
climatology;
comparison O3 from TOMS
and from climatology
Atmospheric
corrections
Data compositing
methods
(see § 3.2, 3.3)
Task 3 :simulation of TOA reflectances for various
aerosol loadings; algorithms of correction of aerosol
effects using B0; validation using the PHOTONS
network + POLDER data
(see § 3.4)
Task 4: Use of GVI and
AVHRR data to compare
"filtering / selecting"
methods such as MVC,
BISE, etc.; validation with
profile shapes (see § 3.5)
Task 5 : Test of several
data compositing methods
on local AVHRR data ;
validation with
meteorological and BRDF
from airborne POLDER
(see § 4.2)
Task 6 : analysis of perturbating effects : natural
variability of surface reflectances, coupling atmosphereBRDF
(see § 4.3)
Table 1 : tasks and planning
6
Dec. 98 - Dec. 99
Task 7 : preparation and
validation of improved
VGT products; synthesis
of previous steps
concerning atmospheric
corrections and data
compositing methods; test
and validation of
improved VGT products
on well documented sites
(Alpilles, Hapex,
BOREAS), using BRDF
and meteo data; additional
validation with data
smoothness, and
comparison with spatial
POLDER data.
(see § 6)
2. DATA SIMULATION PLAN
This Section summarizes the data used to complete the investigation.
2.1 Satellite data
In order to simulate RED and NIR VEGETATION data, we use daily NOAA/AVHRR 1km data set over the
HAPEX-SAHEL test site, for 1991 and 1992, BOREAS site, for 1994, and Alpilles site, for 1996.
The HAPEX-Sahel data set consists of geometrically corrected mid-afternoon HRPT acquisitions of AVHRR by
the AGRHYMET receiving station. Three main levels of processing are available on the CD-ROM produced by
Kerr et al. (1993) : raw data, top of the atmosphere reflectances and brightness temperature, and surface
reflectances and temperatures. We use here the surface reflectances. Atmospheric corrections have been based on
an update of the SMAC method (Rahman and Dedieu, 1994) based on 5S (Tanré et al. 1990). The water vapor
content used in the correction was taken from ECMWF data on the area, whereas the ozone content was derived
from a climatology. A constant aerosol optical depth of 0.23 at 550 nm was applied, and the aerosol model was
taken to be that of continental aerosols. An average of the radiometry has been made on a 3 x 3 pixels area
surrounding the Central West site. Figure 2.1 illustrates the time series of atmospherically corrected AVHRR
data obtained on the Central West site, in the visible and near infrared.
The BOREAS data set consists of GEOCOMP’s (Geocoding and Compositing System) (Czajkowski et al., 1997)
processing to map AVHRR data to a 1-km² equal area projection. A forested site was selected on the Southern
Study Area of BOREAS, the Old Black Spruce (OBS) site, from April 3 to October 6 1994. The GEOCOMP
products are available from BORIS, the BOREAS data base. The atmospheric correction on these data has been
achieved at CNRM using the 6S code, with as inputs a water vapor content and an aerosol optical depth time
series from BORIS. Figure 2.2 show the time series of AVHRR data in the visible and near infrared for the OBS
site.
The Alpilles site data set consists of geometrically corrected mid-afternoon HRPT acquisitions of AVHRR by the
CMS Lannion receiving station. Atmospheric water vapor and ozone corrections are performed on these data by
CMS Lannion, using water vapor and ozone concentrations derived from ECMWF data. No aerosol correction is
made. The data for 1996 have been acquired by CNRM in Toulouse. Figure 2.3 shows the time series of AVHRR
data on this site, on an area 3 x 3 pixels.
Global scale analysis is performed using weekly GVI data set (Tarpley et al., 1984) for the period 1989-1990.
We have developed methods to re-process this data set, described in Section 4. Figures 2.4 and 2.5 illustrate
respectively seasonal variations of GVI, and various processings made on GVI : calibration, atmospheric
correction, cloud filtering (Section 4).
Top of atmosphere POLDER/ADEOS data from November to December 1996 have been selected over two
Sahelian sites equipped with sunphotometers for tests of aerosol corrections.
7
Figure 2.1 : Times series of atmospherically corrected AVHRR data on the Central West site of HAPEX-Sahel from April to
October 1992. The radiometry was averaged for 3x3 pixels.
Figure 2.2 : Times series of atmospherically corrected
AVHRR data on the Old Black Spruce site of BOREAS
from April to October 1994. The radiometry corresponds
to 1 pixel.
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Figure 2.3 : Times series of atmospherically corrected AVHRR data on the Alpilles site for the whole year 1996.
The radiometry corresponds to an area of 3 x 3 pixels.
8
Figure 2.4 : seasonal variations of NDVI
9
Figure 2.5 : various processings of GVI : raw NDVI, NDVI after calibration, NDVI after atmospheric corrections, and finally
after cloud screening.
10
Figures 2.6 and 2.7 : Map of the hemispherical reflectances from airborne
POLDER over a .5 x 10 km² area surrounding the Central West site of
HAPEX-Sahel. Values are lower than 0.1 (0.25) for black pixels and
larger than 0.25 (0.40) for white pixels in the visible (near infrared).
2.2 Airborne data
Airborne data from the POLDER instrument have been collected over a series of natural ecosystems and crops in
the framework of international (HAPEX-Sahel 92, BOREAS 94), and national (Alpilles 96) field experiments.
The airborne version is very similar to the spaceborne instrument which has been launched by ADEOS in August
1996.
In the HAPEX-Sahel experiment, 5 flights of interest for surface monitoring were acquired on August 24,
September 3, 13, 17, and October 4, 1992 (Roujean et al., 1997). A typical POLDER flight consists of 5 parallel
flight lines within the principal plane (solar plane), then of a flight line within the perpendicular plane crossing
the pattern in its middle. A flight line is 15 km long and the nominal flight altitude of the ARAT carrier is 4500
m. The distance between each flight line was chosen to be 2 km in order to observe a given target on the central
line from all other flight lines. Therefore, the BRDF is well sampled for targets located in the vicinity of the
center of the area for a given solar zenith angle, with more than 30 BRDF measurements per target. A 6S type
atmospheric correction is carried on the POLDER images thanks to in situ sunphotometer measurements acquired
routinely during POLDER flights.
For all flights, the images were projected on a Mercator grid and processed so as to reconstruct a BRDF on each
pixel of size 25 x 25 m2 of a 7,5 x 10 km2 area. Then, an adjustment was made for each pixel between the 3parameter Roujean et al. (1992) model and the measured BRDF datasets. Maps of visible (670 nm) and near
infrared (864 nm) hemispherical reflectances are shown in Figures 2.6 and 2.7 respectively for the Central West
11
Figure 2.8 : surface BRDF, derived from POLDER
measurements, on September 13 for 3 selected sites
: shrub fallow, millet crop, degraded shrubland, at
670 nm. The measurement points, which have been
acquired with a mean solar zenith angle of 52.5°,
are displayed and indicate the flight axis. The lines
are for the model estimates, for a solar zenith angle
of 52.5°, after inversion of the Roujean et al. model
on the measurements.
Figure 2.9 : same as Figure 2.8, in the near infrared.
12
Figure 2.10 : map of hemispherical reflectances
derived from airborne POLDER on July 21, 1994 in
the visible over a 5 x 5 km2 area at 100 m
resolution surrounding the Old Black Spruce site of
BOREAS (South Area). Hemispherical reflectances
are lower than 0.01 for black zones and higher than
0.06 for white zones.
Figure 2.11 : same as Figure 2.10 in the near
infrared. Hemispherical reflectances are lower than
0.1 for black zones and higher than 0.25 for white
zones.
Figure 2.12 : directional and spectral diagram for
the Old Black Spruce site on May 31, at 550nm
(top), 670nm (middle) and 864nm (bottom),
corrected for atmospheric effects.
13
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Figures 2.13 and 2.14 : maps of hemispherical
reflectances in the visible and near infrared derived from
airborne POLDER on July 3, 1996 in the visible over a 5
x 5 km2 area at 100 m resolution on the Alpilles site.
Hemispherical reflectances are lower than 0.05 (resp.
0.20) for black zones and higher than 0.35 (resp. 0.65)
for white zones, respectively in the visible and near
infrared.
Figure 2.15 : directional and spectral diagram for the
Alpilles site on July 3, 1996, at 550nm (top), 670nm
(middle) and 864nm (bottom), corrected for atmospheric
effects.
14
site of HAPEX-Sahel where dominant vegetations are shrub fallow, grassland, millet crops and tiger bush. The
hemispherical reflectances are evaluated at the sun zenith angle which corresponds to the average sun zenith
angle encountered in the experiment. The hemispherical reflectances are simply linear functions of the 3
parameters retrieved from the regression analysis (see Roujean et al., 1992).
It is shown in Figures 2.8 and 2.9 the BRDF as represented by the model together with the measurements
acquired on September 13 for shrub fallow, millet crop and degraded shrubland. The solar zenith angle is 52.5°
which is the mean value during the flight. Backscattering is the main directional signature of the surface
reflectance at all wavelengths and the model reproduces the surface directional signatures observed with
POLDER.
In the BOREAS experiment, POLDER flew onboard a C-130 airplane from NASA Ames over « tower sites » of
the South Study Area in Spring and Summer 1994 (Bicheron et al., 1997 ; Bréon et al., 1997). Each target was
overflown several times, each time at a different heading, in the principal, perpendicular, and oblique planes. An
application was to generate BRDF data sets for each surface pixel over an area of size 5 x 5 km2 with a spatial
resolution of 100 m. Typically 10 ground control points were used for each image. This has been applied to the
datasets of Young Jack Pine and Old Black Spruce, acquired on July 21. As for the HAPEX experiment, the
BRDF of each pixel was adjusted against the Roujean et al. (1992) model, and an estimation of hemispherical
reflectances in the visible (Figure 2.10) and near infrared (Figure 2.11) was made.
The atmospheric correction algorithm 6S (Vermote et al., 1996) was applied to the measured reflectances over
« tower sites » to derive reflectances corrected for atmospheric effects. A midarctic summer atmospheric model
and a continental aerosol model were selected to characterize the atmosphere above the BOREAS sites.
Moreover, the total aerosol optical depth for the full atmosphere and the below-aircraft aerosol optical depth,
both at 550 nm, necessary inputs of the algorithm, were obtained from the BORIS database. Figure 2.12 display
BRDFs at 550 nm, 670 nm, and 864 nm for the Old Black Spruce site, corrected for atmospheric effects. A
strong « hot spot » feature is apparent at all 3 wavelengths.
The Alpilles 96 experiment has consisted of 8 flights of the POLDER instrument onboard a Piper-type PA-28
plane on an agricultural site of corn, sunflower and wheat in the Alpilles region, close to St-Rémy de Provence.
The flights (May 14, May 22, May 23, June 7, June 15, June 24, July 3, July 11) took place during the growth of
the vegetation. There were associated measurements of atmospheric optical depth using both an automatic
CIMEL sunphotometer, and a manual sunphotometer operated during the flights. Leaf Area Index measurements
were made 2 days per week, for 8 parcels per day, from May 10 to July 13. One field of corn, 2 fields of
sunflower, 1 field of tomatoes, and 1 field of alfalfa were monitored regularly once a week. In addition, PAR
above and below the canopy was measured on 4 parcels from May 10 to June 20 : alfalfa, sunflower, wheat and
orchard. The processing of the entire data set is currently in progress. The POLDER data set of July 3, 1996 has
been used in this study. Figures 2.13 and 2.14 represent maps of hemispherical reflectances for the July 3, 1996
dataset, in the visible and near infrared, and Figure 2.15 represent an example of BRDF on a field of sunflower.
2.3 Sun photometer network
The AERONET (AErosol RObotic NETwork) sunphotometer system has been developed to provide aerosol
information for atmospheric correction of satellite data and to begin to understand aerosols properties. Handheld
instruments were deployed in the past. In addition to the scientific value of the collected data, valuable
experience was gained and issues of calibration, measurements frequency and logistic were addressed and
learned, (i) first an automatic system was necessary to reduce the variability in frequency and quality of
measurements obtained from on-site observers, (ii) secondly data had to be retrieved and processed in a timely
manner to avoid large gaps in the data stream, (iii) lastly, sky radiance in addition to sun observation were needed
in terms of quantification of aerosols properties.
The entire measurement has grown to approximately 50 instruments world wide (see Figure 2.16). Instruments
are owned by various organizations and PI's. Several instruments are permanently located in selected islands and
land-based sites for characterization of long-term aerosol properties, the others are deployed in support of
specific field campaigns.
15
AERONET PERMANENT AND
SEASONAL SITES, ‘96
Figure 2.16 : map of permanent and seasonal sites of sunphotometers of the AERONET network.
2.4 Miscellaneous
TOVS data for ozone analysis are available on a CD-ROM distributed by NOAA. Climatologies of water vapor
and ozone are already available. The data from weather forecast models, necessary for the study of atmospheric
correction of water vapor, have been acquired through CNRM.
16
3. ATMOSPHERIC CORRECTIONS AND CLOUD SCREENING
The correction of the atmospheric effects for the VEGETATION data implies to have available a method which
is both accurate and fast to be able to process and provide every day a global dataset of surface reflectances. This
chapter presents the method used to correct the signal received by the sensor for the effects of the atmosphere,
the choice of the atmospheric components entering the procedure of correction of atmospheric effects, a
procedure of aerosol retrieval, and some considerations about cloud screening.
The method used to correct the Top Of Atmosphere reflectances for atmospheric effects is the SMAC method
(Rahman and Dedieu, 1994), which has been upgraded to be available for VEGETATION spectral bands.
Results and plots illustrate the method in § 3.1.
The need for global and accurate datasets describing the state of the atmosphere to make the correction of
atmospheric effects is important. One of the problems concerns the choice of sources for the water vapor and
ozone contents which have to be available at the same time as the image acquisitions. This problem is examined
by comparing the accuracy on the estimation of the surface reflectances when using as input of the code different
datasets of atmospheric components (§ 3.2 and 3.3).
A procedure of aerosol retrieval and aerosol correction of reflectances using mainly the blue band of
VEGETATION is described, and validated using simulations and spaceborne POLDER data, in § 3.4. Finally,
some experience applicable to VEGETATION has been gained on cloud screening techniques using the LASUR
data set. This is reported in § 3.5.
The effects of absorption by Oxygen (O2), carbon dioxyde (CO2), methane (CH4), carbon monoxyde (CO) and
nitrogen dioxyde (NO2) are weak or null and will not be discussed further here. Oxygen absorbs solar radiation
in B2 and B3 bands, CO2 and CH4 absorb solar radiation in MIR band. Their gazeous trasmission is taken into
account in the SMAC method. Although the total amount of these gases in an atmospheric column depends on
the atmospheric pressure (therefore on altitude), the modeling of gazeous transmission does not take into account
pressure variations because they have low effects on the retrieval of surface reflectances.
3.1. SMAC upgrading
The SMAC method correct the top of atmosphere reflectances for absorption by gases and scattering by
molecules and aerosols. Radiative transfer in the atmosphere is modeled by semi-empirical formulations.
Absorption and scattering processes are represented by equations which coefficients are fitted using reference
computations made from the 5S and 6S radiative transfer codes. These coefficients are computed for each
equation and each spectral band of VEGETATION channels, i.e. the B0 ([0.42-0.50]µm), B2 ([0.60-0.74]µm),
B3 ([0.74-0.95]µm) and MIR ([1.62-1.76]µm) channels.
Since the accordance of SMAC with 5S was good in terms of accuracy of the estimation of surface reflectance,
SMAC method has been upgraded by fitting the coefficients against 6S computation results. The surface
reflectance is estimated using Eq. 1:
s 
toa  s ,v , ,    tg  a  s ,v , ,  
tg  T  s ,  T v ,    ( toa  s ,v , ,    tg  a  s ,v , ,  ) s
(Eq.1)
where s and v are the solar and view zenith angles respectively,  is the relative azimuth between solar and
view plans, toa is the top of atmosphere reflectance, tg is the total gazeous transmission, a is the atmospheric
reflectance, T is the total atmospheric transmission for the upward and downward path, s is the spherical albedo,
 is the optical thickness of the atmosphere, s is the surface reflectance.
17
Compared to the SMAC method based on 5S, there are modifications which appeared in the formulation of some
analytical equations, particularly, the modeling of the atmospheric reflectance.
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Figure 3.1
(a1) Gazeous transmission of the water vapour for the B2 band versus the air mass times the water vapor content. (a2)
Comparison of the SMAC estimation of gazeous transmission versus the 6S gazeous transmission.
(b1) spherical albedo estimation versus the optical thickness at 550 nm. (b2) Smac estimation of the spherical albedo versus
6S estimation.
18
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Figure 3.1 : (c1) Aerosol optical thickness estimation versus the aerosol optical thickness at 550 nm. (c2) SMAC Optical
thickness estimation versus 6S estimation.
(d1) Atmospheric reflectance estimation from SMAC versus 6S.
19
Table 2
Water vapor content
Ozone content
Aerosol optical depth at 550 nm
Solar zenith angle
View zenith angle
Relative azimuth angle
T.O.A reflectance
<0, 6.5> g cm-2
<0, 0.7> cm atm
<0, 0.5> for B0, <0,0.8> for B2, B3, MIR
<0,60> degrees
<0,
0.8>degrees
for B2, B3, and MIR
<0,60>
<0, 180> degrees
0.15, 0.3, 0.5
SMAC provide a pretty simple parameterization of every term of the Eq. 1. Geometry parameters and top of
atmosphere reflectances are directly provided by the VEGETATION sensor or ground segment, and external data
have to be provided by users to characterize the atmosphere, that is, ozone content of the atmosphere, water
vapour content of the atmosphere, and aerosol content (aerosol optical thickness at 550 nm) of the atmosphere.
A large dataset covering a fair amount of angular and atmospheric conditions has been created to estimate the
accuracy of the SMAC method versus the 6S reference model (Table 2). Based on this important number of
simulations, results of SMAC versus 6S are compared in terms of root mean square error (R.M.S.E) on the
estimation of surface reflectances.
Whatever the spectral band is, the estimation of every term of the Eq. (1) is very good and did not pose any
problems, except for the estimation of the atmospheric reflectance. This is discussed hereafter. The maximum
relative error for the estimation of i) the gazeous transmission of water vapour, ozone, oxygen, carbon dioxyd, or
methan is lower than 0.1 %, ii) 5% for the spherical albedo, iii) 2.2% for the total transmission, iiii) 0.02% for the
aerosol optical thickness.
The parameterization of the atmospheric reflectance lead to R.M.S.E of 0.0040, 0.0029, 0.0018, and 0.0005 for
B0, B2, B3 and MIR channels respectively. Compared to the results found with the SMAC method based on 5S,
the accuracy of the estimation is a little bit less good because the atmospheric reflectance in 6S is computed using
the successive order of scattering method. The multiple scattering by molecules and aerosols were not taken into
account in the 5S code. This is done in 6S, that is why the parameterization is more complicated and not easy to
fit by one analytical equation. Figure 3.1 illustrates some of the results for the B2 spectral band.
As final results, the estimation of the surface reflectance with SMAC leads to the following R.M.S.E. : 0.0057,
0.0049, 0.0037 and 0.0008 for B0, B2, B3 and MIR channels respectively. The errors are stronger when the
surface reflectance is low or when the optical thickness of the aerosol and the air mass are high.
Compared to the analytical equation for atmospheric reflectance, better results are obtained if the 6S successive
order of scattering routine is used for an equivalent wavelength. However, this routine requires more CPU time
than the analytical equation (by about a factor 10), and may not be suitable for operational processing. This
solution, as well as the use of atmospheric reflectance look-table is still to be investigated.
3.2. Water vapor
Atmospheric water vapor content is highly variable both in time and space. Mean variations are relatively well
know and captured by climatologies where observation network is dense. This is not always the case and
climatologic means can be very different from the observed values, for example in regions that encountered
variable monsoon regimes. This will be shown on several sites hereafter.
Different datasets are available for the study:
1) climatologic water vapor (Oort, 1983)
2) water vapor content provided by weather forecasting numerical models ECMWF for 1987.
3) water vapor content estimated by short term weather forecasts (from May to October 1992 over the
HAPEX experience sites in Niger)
20
The relative error on the surface reflectance estimation is given by



tg tg (UWc lim)  tg (UW )

tg
tg (UWc lim)
(Eq.2)
where UWclim are the climatologic mean value and UW the water vapor content estimated by weather model.
The gazeous transmission is computed for the B3 band of VEGETATION which is the most sensitive to water
vapor absorption.
We have extracted the water vapor content from the Oort and ECMWF datasets, for 4 sites located in
Madagascar, France, Canada and over the HAPEX site. The temporal evolution over one year can be seen on
Figure 3.2. The gazeous transmission of both contents has been computed and the relative error on the surface
reflectance estimated using Eq. (2) (Figure 3.3).
ECMWF values are close to the climatologic data. The relative error is globally low ( 1% to 2%). However, one
can see in the sahelian zone (for instance, HAPEX site) and in locations where a few data are available
(Magdagascar site) that the mean water vapor content may differ from the climatology, leading to relative error
about 5%. This means that for an absolute level of reflectance of 0.1 , the error will be 0.005, and for a
reflectance of 0.5, the error will be 0.025.
From the available daily data on the HAPEX site, we have extracted the climatologic water vapor content. Figure
3.4 shows the daily variability of the water vapor content estimated at 12h TU (circles) and the evolution of the
climatologic content (solid line). Although the climatology captures the main variation of the water vapor
content, the daily variability is high and is sometimes far from the climatology. This is well seen at the beginning
and at the end of the period where the prediction are lower than those of the climatology. Relative error is more
important and can reach 5 %.
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Figure 3.2 : Temporal evolution of the water vapor content over 1 year for the climatology (solid line) and
ECMWF data (dashed line) for 4 sites in Madagascar, France, Canada and the HAPEX site.
21
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Figure 3.3 : Relative error on the surface reflectance estimation, for the same 4 sites as in Figure 3.2.
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Figure 3.4 :Temporal evolution on the HAPEX site of the daily water vapor content (a), gazeous transmission (b) and relative
error on surface reflectance (c). Climatologic values are represented by solid line.
22
3.3 Ozone
This study aims at assessing the temporal frequency of ozone data to use in the atmospheric correction code to
estimate the surface reflectance with a sufficient accuracy. At the present time, differents sources of data are
available (climatologies, daily satellite data, monthly means ...).
The objectives of the study are to specify which data use to correct the reflectances from ozone absorption.
Globally, stratospheric ozone (O3) absorbs solar radiation mainly in the red band of VEGETATION (B2), and
weakly in the blue band (B0) of VEGETATION.
Data
Eleven years of monthly means of ozone content from November 1978 to December 1989, and associated
standard deviation have been provided by CNRM. Ozone content is derived from TOMS instrument (Total
Ozone Mapping Spectrometer). Spatial resolution is 1.25 in longitude (288 values) et 1 in latitude (180 values)
over every part of the globe, except in the polar night where there are no values. Data have been resampled to a 1
degre by 1 degre resolution.
A climatology has been prepared from the 11 years of monthly mean of TOMS ( Eq.3).
O3 
1
 O3
11 11 years
(Eq.3)
The gazeous transmission tg(O3) has been computed in the B2 spectral band which is the most sensitive to ozone
absorption. The sensitivity study is only made for that band.
Method
The error on the estimation of the surface reflectance (rs) is assessed by computing the relative error on the
gazeous transmission tg(O3) when a climatologic value is used instead first a monthly value of ozone content, and
second when a climatologic value is used instead a daily value of ozone content.
Monthly variability. The monthly variability of gazeous transmission is estimated by computing the largest
difference between the gazeous transmission of the mean content over the eleven year and the gazeous
transmission of the minimum or maximum of the monthly mean ozone content (Eq. 4). The relative error on the
reflectance is given by:



tg
tg (O3)  tg (O3 min) tg (O3)  tg (O3 max)
 MAX (
,
)
tg
tg (O3)
tg (O3)
(Eq.4)
Daily variability. The daily variability is estimated by computing the largest difference between the gazeous
transmission of the mean ozone content and the gazeous transmission weighted by the maximum of the standard
deviation observed in the 11 years of data (Eq. 5).



tg (O3)  tg (O3  2 O 3 ) tg (O3)  tg (O3  2 O 3 )
tg
 MAX (
,
)
tg
tg (O3)
tg (O3)
(Eq.5)
O3max and O3min are the maximum and the minimum of ozone observed over the 11 years, and tg(O3),
tg(O3min) et tg(O3max) are the gazeous transmissions associated to O3moy, O3max, et O3min respectively.
Climatology variability
Figure 3.5 and 3.6 represent the monthly variations of ozone content and the mean standard deviation. The spatial
variability is mainly latitudinal except in winter in the northern hemisphere where some variations appear with
the longitude. The maximum is located at 50 north during the March and April months, and is minimum in
October. During the year, low variations appear around the equator zone. In the southern hemisphere, the
maximum is not located near the pole like in the northern hemisphere but around 55  S in October. From the
23
Figure 3.5 : Temporal evolution of the climatologic ozonz content
24
Figure 3.6 : Temporal evolution of the standard deviation of the mean ozone content
25
Figure 3.7 : Monthly variability of the mean ozone content
26
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Figure 3.8 : Daily variability: estimation the relative error on the surface reflectance when using a climatologic data instead
of daily data.
27
Figure 3.9 : Histogram of the relative error on the surface reflectance estimation. X-axis is the error in %, Y-axis is the
number of points.
28
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Figure 3.10 : Histogram of relative error on the surface reflectance. X-axis is the relative error in %, Y-axis is the number of
points.
29
Equator to 55 S, the standard deviation increases regularly. Beyond 60 S, ozone content decreases towards the
pole with a minimum during September and October (Ozone hole). The standard deviation is maximum in
October due to the important gradients.
Comparison of TOMS climatologic data and monthly data
The relative error on the surface reflectance if we used climatologic data instead of monthly data is estimated by
Eq. (4). Results are represented only for a vertical viewing but they differs very little for an oblic viewing (Figure
3.7). One can see that the relative error is weak (less than 1 %) for every month and every latitudes except in the
polar zones where the variability is most important. The histogram (Figure 3.8) confirms the low level of the
relative error on the B2 surface reflectance.
Comparison of TOMS climatologic data and daily data
The daily variability of ozone content is estimated by Eq. 4 and results are represented on Figures 3.9 and 3.10.
One can see that the relative error is higher in this case than in the previous one. But the relative error is lower
than 2% between 50  north and 50  south. It is about 3.5 % in October in the southern hemisphere which is the
month where the variability is the most important. Variability higher than 5 % are observed during February and
March in the southern hemisphere but they are not considered by the VEGETATION data acquisition.
In summary, compared to the use of daily data, the use of a climatology leads to relative errors on the estimation
of surface reflectance less than 2%. We recommend the use of a climatology to correct the TOA reflectance from
ozone absorption.
3.4 Aerosols
3.4.1 Sensitivity of blue band to aerosol optical thickness
Atmospheric scattering of solar radiation is due to molecules and aerosols. It has a strong impact on surface
reflectance retrieval, especially in the visible part of the solar spectrum. Molecular scattering is well modelized. It
depends on surface pressure, but in practice it is sufficient to account for surface altitude. Aerosol scattering is a
more difficult issue, since aerosol amount and optical properties are highly variable in space and time. It is
therefore highly desirable to estimate the scattering effect of aerosols on the measured radiances in order to
improve the accuracy of surface reflectance estimates.
Atmospheric scattering increases when the radiation wavelength, decreases, with a -4 dependency for
molecular scattering, and approximately a -1 dependency for aerosols. This means that for sensors such as
VEGETATION, atmospheric scattering decreases from the blue to the middle infrared spectral bands.
Consequently, any algorithm aimed at deriving aerosol characteristics should use the blue band provided that
molecular scattering is properly accounted for.
However the blue band alone, with one viewing direction, does not provide enough information to retrieve
aerosols properties. Neglecting the effect of target environment, top of the atmosphere (TOA) reflectance, *,
can be written as a function of the surface reflectance, c, that we wish to retrieve :
* = tg [ a + T(s).T(v).c/(1 - c.s)]
Eq.(6)
In this equation, tg is the total gaseous transmission (downward and upward path) which takes into account the
various gaseous absorptions, T is the total transmission (diffuse+direct) for scattering processes, s is the spherical
albedo of the atmosphere, and a is the atmospheric reflectance. s and v are solar and view zenith angles,
respectively. Every term within brackets depends on molecular and aerosol scattering.
In this equation, assuming that gaseous absorption is known, we have one measurement * and at least five
unknowns, namely the surface reflectance, the aerosol optical thickness (AOT, also named aerosol optical depth),
phase function, single scattering albedo and Ängstrom coefficient. There is no single relationship between blue
reflectance at the Top of the atmosphere (TOA) and aerosol optical thickness, as illustrated by Figure 3.11.
30
The only way to solve the equation is to add information, would it be coming from several spectrals bands,
multiple measurements or external informations and assumptions. In the approach we propose, we use a
combination of these possibilities.
Figure 3.11 : Top of the atmosphere reflectance in blue
channel as a function of aerosol optical thickness, for surface
reflectances in the range 0.05-0.30.
Figure 3.12 : Top of the atmosphere difference of blue and
red reflectances as a function of aerosol optical thickness, for
surface reflectances in the range 0.05-0.30, identical for the
two bands
3.4.2 Principles of the proposed algorithm
Equation (6) can be rearranged to express the surface reflectance in band k as a function of the other terms.
  *k   k t k 
a g

k
c 
 t k T k ( )T k ( )    *k   k t k  s k 
 g
s
v 
a g  

Eq.(7)
If the atmospheric gaseous content is supplied by external source of data, we can easily compute gaseous
transmission for any spectral band. Molecular scattering can also be computed accurately. Assuming that we
know the aerosol type, we can fix their phase function, absorbing properties and Ängstrom coefficient, which
describe the spectral dependency of optical thickness. The only unknown is then the surface reflectance and the
aerosol optical thickness, that is one equation and two unknowns.
If we now consider measurements of two, or more, spectral bands, the effect of aerosol scattering is not identical
in every spectral band due to the spectral dependency of scatterring (fig. 3.12). However, each time we add one
equation similar to equation 7 for one band, we also add one unknown which is the surface reflectance in the new
band.
One way to solve this issue is to consider that the reflectance in one band is related to the reflectance in another
band by a fixed coefficient. This coefficient can be derived from the analysis of a number of in-situ
measurements, and set according to some vegetation type and soil color index. However, the capability to
31
determine this coefficient from the images themselves would lead to a more robust method. The algorithm we
propose perform such a determination through an iterative process applied to a time series of data.
Let us discuss the case where two spectral bands, 1 and 2, are used. The concept can be extended to a larger
number of spectral bands.
At the surface level, we can write :
  *1   1 t 1 
a g

c 
 t 1 T 1 ( )T 1 ( )    *1   1 t 1  s1 
 g
s
v 
a g  

Eq.(8)
1
  *2   k t 2 
a g

c 
 t 2 T 2 ( )T 2 ( )    *2   2 t 2  s 2 
 g
s
v 
a g  

Eq.(9)
2
For a given observation on a specific day, the following equation is always true :
 c1
a
 c2
Eq.(10)
Where a is a dimensionless coefficient.
We already mentionned that gaseous transmission can be easily computed for any spectral band if the
atmospheric gaseous content is known. In practice, existing climatologies are sufficient to estimate ozone
content. Water vapor absorbs radiation for wavelengths larger than 0.7 m and content estimates can be obtained,
for example, from AGCM analysis.
Using radiative transfer code such as SMAC, 5S or 6S, terms of Eq. (8) and (9) that depends on aerosol
properties can be computed for any spectral band if the aerosol model and the aerosol optical thickness at one
wavelength are known. Assuming the aerosol model is known, we obtain a system of two equations with three
unknowns, namely the surface reflectance in the first band, c1, the ratio of reflectances in two bands, a, and the
aerosol optical thickness at a given wavelength, for example 550nm, 550.
  *1   1 t 1 
a g

c 
 t 1 T 1 ( )T 1 ( )    *1   1 t 1  s1 
 g
s
v 
a g  

  *2   k t 2 
a g

a c1 
 t 2 T 2 ( )T 2 ( )    *2   2 t 2  s 2 
 g
s
v 
a g  

1
Eqs.(11)
In order to solve the system of Eqs. (11), a first solution is to fix the value of the a coefficient, and then invert c1
and 550. However, there is little chance to obtain a robust algorithm.
In our approach, we consider several observations of the same target. In the case of VEGETATION, these
observations are acquired over a given time period for which the a coefficient can be assumed constant.
32
The algorithm operates over a time series of reflectance measurements, for example 1 month, and is based on two
iterations. The first, outermost, iteration covers the entire period and is used to retrieve the a coefficient. The
second, innermost, iteration is applied on 550 for each multispectral observation. Each value of 550 is used to
compute c1 and c2 from Eqs. (8) and (9). For a given value of a, we keep the daily values of 550 that minimize
for each day the difference :
 c1  a  c2
Eq.(12)
When all the values of 550 have been estimated, a new value of the a coefficient is computed, as the monthly
average of retrieved surface reflectances in band 1, divided by the same quantity in band 2. A new iteration on
550 starts with this new value of a. Additional constraint can be added. For example, the retrieved surface
reflectance c1 and c2 must be positive or null.
If more than two spectral bands are used, the same algorithm applies, and the merit function is defined as
i n
 c1   ai  ci
Eq.(13)
i 2
Different weighting of spectral reflectances could be introduced.
As a preliminary assessment of the expected accuracy, we applied the algorithm to top of the atmosphere
reflectances simulated for a set of surface reflectances and aerosol optical depths, but only one aerosol model. In
these simulations, we assumed that reflectances in the blue and red channel were identical. Their ratio, equal to 1,
was not retrieved by the algorithm, i.e. was assumed known. The only aim of the optimization algorithm is
therefore to retrieve AOT. These simulations were performed for different levels of sensor radiometric
resolution. As expected, results (Figures 3.13 and 3.14) indicate that the algorithm works better for low surface
reflectances and high radiometric resolution. With the idealized conditions used for the simulation (known
aerosol model and gaseous absorption, perfect calibration, …), the expected rms error on AOT retrieval is on the
order of 0.03 for a radiometric resolution, Ne, of 0.002, and 0.10 for Ne=0.006. When using retrieved AOT
to estimate surface reflectance, results are good for Neon the order of 0.02, but less satisfactory at lower
radiometric resolution. The impact of surface reflectance is weak. Performance of the algorithm regarding both
AOT and surface reflectance retrievals will certainly be lower when applied to actual data, as done in the
following section.
0
.
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0
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R.MS.eronsurfacerflectane(Blueband)
0
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.
0
0
80
.
0
1
00
.
0
1
2
M
e
a
s
u
r
e
m
e
n
t
a
c
c
u
r
a
c
y
(
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e
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
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e
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e
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t
a
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)
Figure 3.13 : simulation of algorithm performance for
AOT retrieval as a function of radiometric accuracy. The
algorithm used difference of blue and red reflectances, and
only retrieved AOT.
Figure 3.14 : simulation of algorithm performance for
AOT retrieval as a function of radiometric accuracy. The
algorithm used difference of blue and red reflectances,
and only retrieved AOT.
33
3.4.3 Results
We applied the algorithm to POLDER data acquired between november 1 st and December 16th, 1996, over the
Banizoumbou (Niger) and Bidi (Burkina Faso) sites. For these two sites, aerosol measurements acquired in the
frame of the PHOTONS/AERONET network were available. We used daily average of in-situ AOT.
We only considered POLDER measurements of intensity in three spectral bands, namely 443 nm (VGT-B0),
670 nm (VGT-B2) and 865 nm (VGT-B3). Atmospheric corrections were based on the analytic version of
SMAC-6S, that is the version of SMAC which is implemented in the VEGETATION ground segment. We
considered the desertic aerosol model.
We used constant ozone and water vapor content derived from climatologies, i.e. constant for the whole period,
and considered every direction of observation provided by POLDER, but separately. The algorithm was applied
to the following combinations of spectral measurements : {443 nm, 670 nm}, {443 nm, 865 nm}, {670 nm, 865
nm}, {443 nm, 670 nm, 865 nm}.
The initial value of the a parameter was set to a=1. 550 was allowed to vary between 0.0 to 1.0 by step of 0.02.
The first interesting result is the rapid convergence of the algorithm to determine the a coefficient, as shown in
figure 3.15 : only 2 iterations are needed to reach a stable value of a.
Figure 3.15 : Values of retrieved reflectance ratios as a function of iteration number. Red/NIR ( respectively Blue/Red or
Blue/NIR) ratio curve is obtained when only Red and NIR (respectively Blue/Red or Blue/NIR) data are used.
Figures 3.16 to 3.18 present examples of aerosol optical thickness retrievals. Results are rather similar for {443
nm, 670 nm} and {443 nm, 865 nm} algorithms, even if the {443 nm, 865 nm} seems to perform better for
medium AOT in Banizoumbou. RMS errors on AOT, computed with in-situ AOT, are on the order of 0.13
(tables 3 to 6). Scattering of the results is rather large, and the main merit of the algorithm is to classify AOT as
low (0.-0.3), medium (0.3-0.50), or large (> 0.5). However, our main objective is not AOT but surface
reflectance retrieval. Accuracy on surface reflectance can be quite different from the accuracy on AOT. Poor
performance of the algorithm for AOT can be partly attributed to a low sensitivity of satellite radiances to AOT.
But this means also that retrieval of surface reflectance is not very sensitive to AOT errors.
Statistics of the results in terms of surface reflectance are given in Tables 3 and 4 for the four possible
combinations of bands. Rms errors on surface reflectances estimated with retrieved AOT are relative to surface
reflectances obtained with in-situ AOT. AOT retrieved with one couple of spectral bands allows to compute
surface reflectances in the others bands, and therefore their ratios.
Results obtained with Blue-Red or Blue-NIR combination are very similar, both in terms of surface reflectance
accuracy and of AOT. As expected, the accuracy is lower with Red-NIR bands, since aerosol scattering is rather
low in that bands.
34
For the full POLDER dataset, i.e. several observations per day, Figures 3.19 to 3.22 compare surface reflectances
estimated with the AOT retrieved by the algorithm and surface reflectances computed with sunphotometer
estimates of AOT, which serve as a reference. The impact of aerosol scattering is low for reflectance of the order
of 0.25. Consequently, the efficiency of the algorithm is more visible in the blue band than in the near infrared
band. AOT and surface reflectance are difficult to retrieve at large AOT. Therefore, the algorithm flags AOT
larger than 0.6. One may notice that this leads the algorithm to discard unreliable surface reflectance estimates or
to give a quality mark.
Until now, we used all available POLDER direction of observations between November 1st and December 16th,
which gives 196 and 309 measurements for Banizoumbou and Bidi, respectively. Since VEGETATION will
generally acquire only one multispectral observation per target and per day, we also applied the algorithm to only
one POLDER observation per day during the period in order to simulate VEGETATION acquisitions. After
cloud screening, this gives 18 and 26 measurements for Banizoumbou and Bidi, respectively. Results are
summarized in Tables 5 and 6. They are rather similar to the results obtained with the full dataset. This means
that the algorithm should be applicable to VEGETATION data.
Figure 3.16 : comparison of satellite retrieval of
aerosol optical thickness to sunphotometer
estimates. Method : difference of blue and red
bands, site : Banizoumbou. Rmse = 0.134.
Figure 3.17 : same as figure 3.16, but method :
difference of blue and near infrared bands.
Rmse = 0.146.
3.5.4 Discussion and conclusion
The algorithm was tested over two sites located in semi-arid regions of western Africa, during the dry season.
Preliminary results indicate that the use of the VEGETATION blue band greatly improves our capability to
estimate at least broad classes of aerosol optical thickness and moreover to apply consistent atmospheric
corrections.
The main advantage of the algorithm is to avoid any assumption on the relationship between surface reflectances
in the various bands, since band ratios are estimated by an iterative process. However we assume that band ratios
are stable during the time period needed to adjust them. We tentatively suggest to use the algorithm with blue and
red bands, instead of blue and near infrared, since we expect that the ratio of blue and red bands is more stable
when vegetation cover or soil color evolves. There are two other reasons for using blue and red bands : a)
surface directional effects are presumably rather similar in both bands, b) water vapor absorption is weak
compared to near infrared, making the algorithm less sensitive to uncertainties on water vapor content. Since the
35
convergence of the algorithm is good, it should be possible to simply model band ratios as a function of time, and
to fit also time function parameters.
Another major assumption of the algorithm is that the aerosol model is known. Apart from using different aerosol
models according to the region, improvements could come from the analysis, and use, of the relationship between
aerosol optical thickness and Ängstrom coefficient. In addition, multispectral information could be more
exploited than done in the current version.
The issues we mentionned above, as well as other such as optimization method, will be investigated using actual
VEGETATION data over different types of targets in order to check the algorithm for a variety of conditions.
The possible implementation of the algorithm in the VEGETATION operational processing has to be studied,
taking into account data flow constraints and interdependence of processes, such as cloud screening, image
compositing and radiometric corrections. However, we believe that the operational implementation of the
algorithm is feasible. It only requires to initialize the process with one or two months of data, and then update it
continuously with newly acquired data.
Figure 3.18 : comparison of satellite retrieval of aerosol optical thickness to sunphotometer estimates. Method :
difference of blue and red bands, site : Bidi. Results of the method based on the difference of blue and near
infrared bands are nearly identical and not presented here. Rmse = 0.128.
Banizoumbou (196 cases)
Rmse
Rmse
Method
Blue
Red
Blue-red
Blue-NIR
Red-NIR
Blue-Red-Nir
0.0193
0.0197
0.0294
0.0197
0.0056
0.0060
0.0089
0.0058
Rmse
NIR
0.0069
0.0075
0.0142
0.0072
Rmse
Blue/red Blue/NIR Red/NIR
optical
thickness
0.134
0.2036
0.7051
0.2888
0.146
0.3004
0.7045
0.2116
0.313
0.3645
0.2600
0.7134
0.143
0.2991
0.2107
0.7044
Table 3 : Statistics of results for the Banizoumbou test-site, when using all available POLDER observations, from
november 1st to december 16th, 1996. Bold numbers correspond to band ratios estimated by the iterative algorithm. Other
ratios are computed from surface reflectances estimated using retrieved aerosol optical thicknesss.
36
Figure 3.19 : Comparison of surface
reflectances in the blue channel estimated with
satellite retrieved and sunphotometer aerosol
optical thickness (AOT), respectively, for the
Banizoumbou test-site. All available data have
been used, i.e. several measurements per day.
Black, filled, dots correspond to top of the
atmosphere reflectances. Gray squares are
surface reflectances. Open circle are surface
reflectance eliminated by the algorithm when
estimated AOT is larger than 0.60. Algorithm
used : blue and red difference.
Figure 3.20 : Same as figure 9, but for the near
infrared band.
Figure 3.21 : Same as figure 9, but for the Bidi
test-site (blue band).
Figure 3.22 : Same as figure 10, but for the
Bidi test-site (near infrared band). Negative
values correspond to cases for which the
algorithm did not converge (AOT=9)
37
Bidi (309 cases)
Rmse
Method
Blue
Blue-red
Blue-NIR
Red-NIR
Blue-Red-Nir
0.0181
0.0182
0.0270
0.0406
Rmse
Red
Rmse
NIR
0.0057
0.0058
0.0069
0.0082
0.0057
0.0053
0.0115
0.0205
Rmse
Blue/red Blue/NIR Red/NIR
optical
thickness
0.128
0.1504
0.6396
0.2352
0.121
0.2463
0.6417
0.1581
0.237
0.3231
0.2110
0.6532
0.450
0.6021
0.4336
0.7202
Table 4 : Statistics of results for the Bidi test-site site, when using all available POLDER observations, from november 1st
to december 16th, 1996. Bold numbers correspond to band ratios estimated by the iterative algorithm. Other ratios are
computed from surface reflectances estimated using retrieved aerosol optical thicknesss.
Banizoumbou (18 cases)
Rmse
Rmse
Method
Blue
Red
Blue-red
Blue-NIR
Red-NIR
Blue-Red-Nir
0.0274
0.0279
0.0374
0.0278
0.0089
0.0091
0.0091
0.0090
Rmse
NIR
0.0072
0.0082
0.0121
0.0082
Rmse
Blue/red Blue/NIR Red/NIR
optical
thickness
0.116
0.2272
0.6919
0.3284
0.134
0.3466
0.6952
0.2409
0.310
0.4218
0.2983
0.7071
0.133
0.3450
0.2397
0.6949
Table 5 : Statistics of results for the Banizoumbou test-site, when using only one POLDER observation per day, from
november 1st to december 16th, 1996. Bold numbers correspond to band ratios estimated by the iterative algorithm. Other
ratios are computed from surface reflectances estimated using retrieved aerosol optical thicknesss.
Bidi (26 cases)
Method
Rmse
Blue
Rmse
Red
Rmse
NIR
Blue-red
Blue-NIR
Red-NIR
Blue-Red-Nir
0.0189
0.0223
0.0426
0.0213
0.0048
0.0055
0.0093
0.0053
0.0034
0.0045
0.0085
0.0045
Rmse
Blue/red Blue/NIR Red/NIR
optical
thickness
0.101
0.2084
0.6344
0.3285
0.126
0.3442
0.6375
0.2194
0.227
0.3832
0.2477
0.6463
0.123
0.3438
0.2191
0.6374
Table 6 : Statistics of results for the Bidi test-site site, when using using only one POLDER observation per day, from
november 1st to december 16th, 1996. Bold numbers correspond to band ratios estimated by the iterative algorithm. Other
ratios are computed from surface reflectances estimated using retrieved aerosol optical thicknesss.
3.6 Cloud screening
Cloud filtering is not straightforward for coarse resolution sensors. Cloud filtering is usually achieved on
AVHRR data by a series of threshold techniques operating both in the thermal infrared and in the visible and near
infrared (e.g. Derrien et al., 1992). VEGETATION does not have any thermal channel. The alternative consisting
of using some a priori knowledge of the temporal evolution of vegetation phenology to discard or retain the data
points, as discussed by Viovy et al. (1992), seems promising. One such alternative is developed below.
To improve the radiometric quality of reflectances and vegetation index (NDVI) time series, a filtering process
has been developed to discard data noised by persistent cloud cover, inaccurate characterization of the
atmosphere, missing data at high latitude during winter and spurious data... The filtering has been made on two
years of weekly Global Vegetation Index data (GVI), 1989 and 1990. Data are collected together on a CD-ROM,
named LASUR for LAnd SUrface Reflectances (Berthelot et al, 1997). Before filtering, reflectances are
calibrated using Kaufman and Holben postlaunch coefficients (1993) and atmospheric effects are corrected using
38
the SMAC method (Rahman and Dedieu, 1994) which is well designed for processing large temporal series of
satellite data.
Data filtering is based on a two steps method (INTUITIV, Loudjani et al, 1994). The first one detects pixels
which are contaminated by clouds, cloud shadows, snow, and missing data or erroneous data. The second one
filters the remaining value and smoothes the temporal profile of NDVI.
Cloud detection is made using thresholds on visible and near infrared reflectances and by comparing the surface
temperature computed from T4 and T5 of AVHRR channels (Kerr et al., 1992) to a climatology of air
temperature (Leemans and Cramer, 1991). Cloud shadow detection is made by comparing the surface visible
reflectance to a minimum value of visible reflectance. This minimum value corresponds to the visible reflectance
value for which the NDVI observed during the year is the greatest. If one of the test fails, pixel is considered as
cloudy, is flagged and removed from temporal time series. Another specific test is performed to identify long
snow period. If no values are retained by the threshold method during more than 7 weeks, pixels are assumed and
flagged as snowy.
The remaining values are then filtered by the BISE method (Viovy et al., 1992) which is designed to smooth the
temporal profiles of vegetation indices. BISE assumes that NDVI increases with plant growth and decreases
during the senescence period or death of the plant, but also when pixel is contaminated by fire or harvest crop
and deforestation. A rapid decrease of NDVI followed by a rapid increase of NDVI is not due to a change of
vegetation state. From these assumptions, increasing and decreasing of the signal are examined over a sliding
period varying between 8 and 14 weeks. Pixels which do not satisfy the method are discarded from the temporal
serie. Figures 3.23 and 3.24 illustrate the filtering of the data on a site located in West Africa. At the end, only
pixels which are marked by a circle are kept in the temporal series. Values which have been removed from the
temporal serie have been replaced by interpolated values. A flag channel indicates the reasons why the tests have
failed.
LASUR data have been sent to 20 investigators or teams to evaluate the quality of the reflectance and NDVI
datasets. There has been no negative reaction as to the quality of the processing. The LASUR data have been
prepared with thermal tests (see above), and one may ask whether these tests are critical or not in view of an
application to VEGETATION data. We have operated tests on the LASUR data, by adding or removing the
thermal tests in the cloud screening. The results show that the thermal tests are not critical.
The temporal filtering proves to be efficient. The BISE method has been used successfully by Running and
Nemani (1997) for an assessment of forest phenology in the United States. On the other hand, the envisioned
sliding period of 8 to 14 weeks seems too long for the operational processing in real time that should be applied
to VEGETATION data. We feel that this period of time should be reduced to typically 4 weeks (1 month), with a
simpler temporal test on the NDVIs.
In addition to NDVI, the blue band may be used for cloud screening : our experience with POLDER/ADEOS
data shows that spurious data (due to partial cloudiness or aerosol events) may be localized and filtered out by
screening the temporal profiles of the blue band. The rationale behind this approach is that clouds are very bright
and surfaces generally very dark in the blue. A thresholding test on the blue has been implemented in the
POLDER processing center (Bréon and Colzy, 1998). With the blue band the cloud screening strategy could be
first to apply a threshold, and then study the temporal profile of the blue in order to remove spikes.
It will also be interesting to use the MIR band, due to its capacity to discriminate liquid phase clouds, as the first
VEGETATION images showed unambiguously. Here, the absolute value of MIR can be used as a cloud
indicator. As is well known, thresholding reflectances simulataneously in the visible/near infrared and in MIR
may lead to a robust discriminator of clouds and snow.
A main limitation of the method seems to be the lack of account of directional effects before the
filtering/selecting method is applied. To account for this effect, it is necessary to use in the filtering process a
data base of BRDF.
39
Titre:
CD/l460c1358_ref.eps
Auteur:
MATLAB, The Mathw orks, Inc.
Aperç u:
Cette image EPS n'a pas été enregis trée
avec un aperç u intégré.
Commentaires:
Cette image EPS peut être imprimée s ur une
imprimante PostScript mais pas s ur
un autre type d'imprimante.
Figure 3.23 : Temporal profile of visible and near infrared top of atmosphere reflectance (grey crosses) and surface
reflectance ( black crosses) for 1989 and 1990. Circles indicate the retained values of surface reflectances after filtering.
Titre:
CD/l460c1358_ndvi.eps
Auteur:
MATLAB, The Mathw orks , Inc .
Aperçu:
Cette image EPS n'a pas été enregis trée
av ec un aperçu intégré.
Commentaires :
Cette image EPS peut être imprimée sur une
imprimante Pos tSc ript mais pas sur
un autre ty pe d'imprimante.
Figure 3.24 : (a) NDVI for 1989 and 1990. Crosses indicate the NDVI values computed from surface reflectances. Circles
indicate the retained values after filtering. (b) Temporal profile of NDVI for 1989 and 1990. Values between 2 circles have
been linearly interpolated.
40
4. CORRECTION FOR ANGULAR EFFECTS
4.1 Position of the problem
It is desirable that the product of the compositing technique be physically a surface reflectance. The high
directional variability of reflectances (see Figures 2.1-2.3) strongly suggests that the compositing should contain
an operation of adjustment of a time series of reflectances, corrected or not for atmospheric effects, during a
given compositing period, against a simple model of bidirectional reflectances. We summarize below some
current attempts to apply a similar philosophy to existing and future wide field of view sensors dedicated (among
others) to the observation of terrestrial vegetation at regional and global scales, and draw from this review several
consequences for the orientation of the present study.
AVHRR. Leroy and Roujean (1994) have adjusted the 3-parameter linear model of Roujean et al. (1992) on time
series of atmospherically corrected AVHRR data acquired on several sites of agricultural fields and forests
located in France. Their compositing and sampling periods were chosen to be 30 days and 10 days respectively.
They replace the time series of AVHRR data by the time series of the first coefficient (named k0) of the model,
which represent the reflectance seen at nadir for a sun at zenith. The time behaviour of k0 (also called normalized
reflectance) was shown to have little high frequency component, and to have a realistic low frequency behaviour.
However, the other parameters (k1 and k2) show a rather erratic time behaviour and are very sensitive to noise in
the data. The high sensitivity to noise of k1 and k2 is interpreted as a consequence of the fact that AVHRR data
occupy a too limited place in directional space, since the sun zenith angle and the relative azimuth between sun
and view directions vary very little during a period of composition. Wu et al. (1995) have employed similar
techniques with some prior land cover type selection over a wide region of Canada including grasslands, forests,
and cultivated areas.
Cabot and Dedieu (1997) have derived with a similar method the hemispherical reflectances with a set of daily
atmospherically corrected AVHRR/NOAA data acquired over a test site located in Niger, north of Niamey
(15.02°N, 3.66°E), from May to October 1991. Since the site is a semi-arid site which shows very small temporal
evolution, the period of composition is chosen to be the whole study period (6 months). A number of different
analytical reflectance models of various philosophies, empirical (Shibayama and Wiegand, 1985), semi-empirical
(Roujean et al., 1992; Rahman et al., 1993), geometrical (Deering et al., 1990), and radiative transfer based
(Hapke, 1981; Ross, 1981; Verstraete et al., 1990) have been tested against the satellite data. The results show
that the accuracy of the adjustment between model and observations is nearly independent of the chosen model.
A rather accurate prediction could be made of the reflectances that an other sensor (METEOSAT-4) would see
on the same target, with a very different sun and view configuration.
Ba et al. (1995) have developed an other concept. The parameters of a temporal and directional TOA reflectance
model, of the form
TOA
Rmod
(t ,  s ,  v , )  R0 F ( t )G( s ,  v , ) ,
are adjusted against the observed TOA reflectance time series over the whole annual vegetation cycle. The basic
assumption implied by this equation is that the temporal and directional behaviour of TOA reflectances are
uncoupled. R0 is the time averaged TOA reflectance of the site. F(t) describes the temporal evolution of the
reflectances corrected from directional effects, and is represented by a 10-parameter Fourier time series.
G(  s ,  v ,  ) is a 2-parameter empirical analytical function and represents the directional behaviour of the
coupled surface-atmosphere system. Ba et al. (1995, 1997) have applied successfully this scheme to AVHRR
data acquired in 1987 over the Konza Prairie during the FIFE experiment, and over the HAPEX site in 1992.
However this method cannot be applied in real or near real time since one year of data have to be accumulated
before the processing can be made.
POLDER. The compositing technique chosen for POLDER describes the surface bidirectional reflectance,
averaged over a sliding compositing period of 30 days, with a limited number of model parameters. The
frequency of production of the composite is 10 days. The compositing process has a two-fold objective: first, to
collect a sufficient number of directional data to constrain the adjustment of the BRDF model, and second, to
41
smooth the time profiles of reflectances to reduce effects of instrumental noise, inaccuracies in cloud detection or
atmospheric correction algorithms, or natural variability of the surface. For this purpose, time series of
atmospherically corrected reflectances, acquired during a period of composition, are fitted with a parameterized
BRDF model. Several models have been tested : (1) the 4-parameter empirical model of Shibayama and Wiegand
(1985), (2) a modified version of the 4-parameter empirical model of Walthall et al. (1985) satisfying the
reciprocity principle, as used by Nilson and Kuusk (1989), (3) the semi-empirical model of Deering et al. (1990)
; only the part of the model describing the reflectance of a bare soil and including 3 free parameters was used (the
complete formulation of the model is not linear), (4) the 3-parameter semi-empirical model of Roujean et al.
(1992), and (5) a linear decomposition of the BRDF on a basis of spherical harmonics, also referred to as
Legendre polynomials (Goel, 1988). This evaluation is described in detail in Hautecoeur and Leroy (1996) and
Leroy et al. (1997), and has lead to the selection of the Roujean et al. (1992) model for the compositing
technique.
MERIS. A different approach was undertaken by Baret et al. (1997) and Weiss et al. (1998) to define
normalization and compositing techniques applicable to MERIS. The study is mainly based on radiative transfer
simulations. Models are run to build a large data base of BRDF at the top of the atmosphere as seen by MERIS,
along with the top of canopy corresponding BRDF, the hemispherical reflectance, the albedo, the cover fraction
as well as the fraction of absorbed PAR (fAPAR). The models used are PROSPECT for the leaf optical
properties, Hapke’s model for the soil, Mineny’s 3-D radiative transfer model, and 5S for the atmosphere. The
data set mimics the BRDF of 4 great biomes (grasslands, tropical forests, boreal forests, sparse vegetation plus
the corresponding bare soils) at various seasons and at various latitudes.
The normalization technique proposed by Weiss et al. (1998) is similar to that proposed for POLDER. It
consists, after correcting for atmosphere effects, of an adjustment of the simulated directional reflectance against
a linear BRDF model. The idea here is to retrieve from this adjustment an estimate of hemispherical reflectances.
The methodology consists of the following steps : (1) a simulation of MERIS TOA reflectances by taking a
directional sampling of the simulated TOA BRDF taking into account the viewing geometry of MERIS over a
period of 30 days, (2) the use of a cloud climatology to operate at random a data selection to simulate cloud
screening, (3) an atmospheric correction introducing some noise relative to the atmospheric correction made in
direct mode to translate top of canopy into TOA reflectances, (4) a comparative adjustment of the resulting
reflectances with 4 different linear BRDF models (basically the same as for POLDER, see above). The results
showed that Walthall’s model is the most robust for the derivation of hemispherical reflectances, and that the
sensitivity of this product to atmospheric noise and to directional sampling is small.
MODIS and MISR. Wanner et al. (1997) describe the theory and the algorithm to be used in producing a global
BRDF/albedo product from data acquired by MODIS, also using the data from the multi-angle imaging
spectroradiometer MISR to be launched on the same platform. Their original idea was that one of six kerneldriven semiempirical models characterizing (as in Roujean et al., 1992) isotropic, volume and surface scattering
should be used to model the BRDF and the albedo of each pixel of the land surface at a spatial resolution of one
kilometer and once every 16 days in seven spectral bands. The semi-empirical models are all 3-parameter linear
models inspired from various types of models (Cox and Munk, 1954; Ross, 1981; Roujean et al., 1992; Li and
Strahler, 1992; Strahler et al., 1994). Model selection is made as follows. All co-registered MODIS and MISR
data will be considered in a 16 days period and at a space resolution of 1 km. The best-fitting of all BRDF
models is chosen if its root mean square error (RMSE) is low, without recourse to any ancillary data. If its RMSE
is high, then it will still be chosen except if the model suggested for this pixel by an ancillary BRDF data base has
an RMSE that is not much worse.
This first idea was subsequently abandoned and only one model will be used, called Li-sparse Ross-thick by
Wanner et al. (1997).
Retrievals are performed through an iterative step : first, an atmospheric correction is performed, second, an
adjustment between BRDF model and observations is made, third, this retrieved BRDF is used as an input for a
new atmospheric correction removing the assumption of Lambertian surface reflectance, and so on. Validation of
the BRDF models employed is demonstrated using a variety of field-measured data sets for several different land
cover types.
SEVIRI. The instrument SEVIRI onboard the forthcoming meteorological satellite MSG will provide an
innovative characterization of the BRDF thanks to a data acquisition under various solar zenith angles. This
appears as the necessary complement of the multiangular sensor systems to obtain a full determination of the
42
BRDF. Indeed, based upon the reciprocity principle, it is expected that SEVIRI can contribute to about the same
BRDF level quality as other sensors. Kernel-driven models of the reflectance (Roujean et al., 1992; Wanner et
al., 1995) are being considered by one of us (JLR) for operational albedo production. A thorough comparaison of
various models will be carried out to test the sensitivity of the retrieved albedo products to the angular sampling.
During the development phase, the BRDF model of Roujean et al. (1992) which includes a description of shading
effects will be primarily applied to GOES data, then to SEVIRI simulated data sets. The will serve to apraise the
impact of cloud occurence and atmospheric effects as well. The deliverables are from a daily to decade release,
following the production of clouds mask and aerosols on the same time basis. It is anticipated that the frequent
diurnal sampling provided by the geostationary satellites will contribute to improve estimate of the surface
albedo used in the Global Circulation Models (GCMs) since a large extrapolation is needed to derive the required
daily mean albedo from multiangular measurements. A narrow-band to broad-band albedo conversion will be
applied in sequence.
4.2 Test of compositing methods using AVHRR data on selected sites
4.2.1 Issues of interest
The short review of the previous Section permits to give an orientation to the present work, and permits to list the
series of issues that should be solved to obtain a sufficient definition of the algorithm to be implemented in the
VEGETATION image processing center. First, it seems reasonable to consider only linear models in the
compositing. Linear models are sufficient to account for the main characteristics of the directional signature,
particularly when the sharp signatures of hot spot and specular reflexion are most often not observed in the
VEGETATION field of view. Non linear models are at least two orders of magnitude more expensive in terms of
computer time, since a large number of iterations are needed before convergence can be obtained. Non linear
algorithms are also much more complex to implement, since many specific situations occur (non convergence,
parameters going out from their range of likely values, local minima of the cost function, etc.). These models
seem to be worth using when the objective is to retrieve surface biophysical parameters using many different
spectral and directional informations in input, not when the objective is to correct the data for angular effects
using in input a relatively small number of directional data.
Assuming the model linear, its choice is a possible issue. The models adopted for various sensors depend on their
directional sampling configuration : as discussed above, Roujean’s model and Walthall’s model have been shown
to be most appropriate respectively to the POLDER and MERIS configurations, while the model adopted for the
MODIS + MISR albedo product is the so-called Ross-thick Li-sparse model (Wanner et al., 1997). A choice has
to be made for VEGETATION. It seems natural to test and intercompare these 3 models which have been used in
various satellite experiments.
A second issue concerns the definition of the compositing product. Hemispherical reflectances are recommended
(at least as part of the delivered products) for several instruments (see above). An other possibility, which has
been proposed for AVHRR, is to produce the bidirectional reflectance seen for a particular sun-view geometric
configuration. The choice of Leroy and Roujean (1994), i.e., the reflectance seen at nadir with a sun at zenith,
may be perhaps inadequate since this particular geometry corresponds to the « hot spot » geometry, the physics of
which are only partially taken into account in the physics of the linear BRDF models used in this study. Thus the
choice should be between hemispherical reflectances, and a bidirectional reflectance in some sun-view geometry
to be defined, but anyway not too different from the average sun-view geometry of the VEGETATION data, to
avoid errors due to strong extrapolations of the model predictions.
Another issue concerns the length of the compositing period to be considered. The baseline here is to consider 30
days of data. Our experience so far with POLDER data processing (Leroy et al., 1998) is that a 30-day
compositing period is appropriate for most types of biomes, except for some tropical forests for which the
number of available cloud free orbits in a 30-day period may be null or a few units. This experience also shows
that in many biomes the vegetation activity undergoes strong variations during such a period. Thus 30 days
appears as a fair compromise for a duration which should be long enough so that enough data are considered, and
short enough so that the vegetation changes remain moderate.
43
Also, two important potentially perturbating factors have to be considered : the fact that temporal variability of
reflectances may occur during the compositing period, and the inaccuracies involved assuming a Lambertian
surface reflectance in the atmospheric correction. These errors are evaluated in the next Section (§ 4.3).
The issues of the choice of directional model and of the choice of product (hemispherical or bidirectional
reflectance) are discussed below.
4.2.2 Test of different BRDF models
We have tested 3 different BRDF models on the AVHRR data set of each of the 3 selected test sites. The 3
BRDF models assume the form :
(s , v , )  k 0  k1 f 1 (s , v , )  k 2 f 2 (s , v , )
Walthall’s model (Walthall et al., 1985) : the two kernel functions are defined as :
f 1 ( s ,  v ,  )   v cos 
f 2 ( s ,  v ,  )   v 2
Roujean’s model (Roujean et al., 1992) : the two kernel functions are defined as :
1
 (    ) cos   sin  tan s tan v  (tan s  tan v  G ) / 
2
4 (  / 2  g ) cos g  sin g 1
f 2 ( s ,  v ,  ) 

3
cos  s  cos  v
3
f 1 ( s ,  v ,  ) 
with
G  tan 2  s  tan 2  v  2 tan  s tan  v cos 
g  cos 1 (cos  s cos  v  sin  s sin  v cos  )
Li-Ross’s model (called Li-sparse Ross-thick model by Wanner et al., 1995) : the two kernel functions are
defined as :
1
1
1  cos g '


cos  s ' cos  v ' 2 cos  v '
(  / 2  g ) cos g  sin g 
f 2 ( s ,  v ,  ) 

cos  s  cos  v
4
f 1 ( s ,  v ,  )  O 
with
b
 s '  tan 1 ( tan  s )
r
b
v '  tan 1 ( tan v )
r
g '  cos1 (cos ' s cos ' v  sin ' s sin ' v cos )
44
D  tan 2 ' s  tan 2 ' v 2 tan ' s tan ' v cos 
h
t  cos (
b
1
O
D 2  (tan ' s tan ' v sin ) 2
sec ' s  sec ' v
)
1
(t  sin t cos t )(sec ' s  sec ' v )

where b, r, h are parameters characterizing the crown sizes. Here, as in Wanner et al. (1997), b/r = 1 and h/b = 2.
The AVHRR time profiles data have been averaged on areas of 3 x 3 km2 for the HAPEX and Alpilles site, and
1 pixel was selected for the Old Black Spruce site of the BOREAS experiment. This averaging is done to
minimize radiometric artefacts due to misregistration effects. The Old Black Spruce BOREAS region is
sufficiently homogeneous to consider that the misregistration effects are unconsequential.
A regression is applied every 10 days between AVHRR data and BRDF models with a compositing period of 30
days, centered on the dates under consideration, as in Leroy and Roujean (1994).
Figures 4.1 to 4.3 represent, for each of the 3 sites, and for each of the 3 models, time series of the first parameter
k0 in the visible and near infrared, NDVIs derived from the k0, and the squared correlation coefficient.
A first criterion for the selection of a BRDF model is that the compositing should be such as to minimize the
noisy features of reconstructed temporal profiles of composited reflectances and indices. The examination of the
curves show that globally Walthall’s model performs the best for this criterion, then follows Roujean’s model,
and finally Li’s model. In particular, the reconstructed NDVI time profiles for each of the 3 sites seem realistic
with Walthall’s model.
A second criterion is that the magnitude of regression residues must be as small as possible (or equivalently, the
correlation coefficients of the regression should be as large as possible). Figures 4.1 to 4.3 show that in that
respect all 3 models have similar performances. The R2 coefficient is most often satisfactory on the Alpilles site
and for the near infrared channel of the BOREAS site, but most oten mediocre on the HAPEX site and for the
visible channel of the BOREAS site. It is likely that in these latter cases the noise on the data is larger than the
directional signal. However, even in these latter cases, the smoothness of the reconstructed k0 indicate that the
BRDF models are sufficiently robust to provide apparently reasonable solutions.
4.2.3 Comparison of AVHRR/airborne POLDER BRDFs
Another way to assess the BRDF reconstruction process is to compare the reconstructed BRDF from AVHRR
with the directional signature acquired on the test zones by the airborne POLDER. On each of the 3 sites, the
airborne POLDER data are processed to provide a BRDF averaged over a 3 x 3 km zone. This is done by
adjusting a model on the BRDF acquired on every pixel of size 20 x 20 m of the 3 x 3 km zone, and by averaging
the model parameters on the 3 x 3 km zone. With these averaged model parameters, one reconstructs the
averaged BRDF over the selected zone using the same model. This BRDF is considered as the BRDF of
reference. The selected POLDER flights are those of Sept. 13, 1992 over the Central West site of HAPEX, of
July 21, 1994 over the Old Black Spruce site of BOREAS, and of July 3, 1996 over the Alpilles site. One selects
then a monthly period for AVHRR centered as close as possible from the airborne POLDER flight, and
reconstruct a BRDF for this period.
Figures 4.4 to 4.6 represent the comparison between the reconstructed BRDFs of AVHRR and POLDER over the
3 sites, when using either Roujean’s model or Walthall’s model for the AVHRR and POLDER BRDF. The
principal and perpendicular planes have been selected as representative for the comparison of the whole BRDF.
The Figures show that there is a very good agreement between both BRDFs for the BOREAS site on the range –
60° to +60°. For the Alpilles site, the shape of the BRDFs is similar for AVHRR and POLDER for both models.
45
Figure 4.1 : BRDF model comparison. The first model parameter k0 in the visible (top left), near infrared (top right),
NDVI derived from k0 (bottom left) and the correlation coefficient R2 of the adjustement (bottom right) are shown for
the models of Roujean, Walthall and Li-Ross for the Central West HAPEX site.
46
Figure 4.2 : same as Figure 4.1 for the Old Black Spruce site of BOREAS
47
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Figure 4 .3 : same as Figure 4.1, for the Alpilles site.
There is however a serious intercalibration problem, since the reflectances of AVHRR and POLDER differ by a
factor 2 in the near infrared, and 1.5 in the visible. The reasons of this discrepancy are not understood yet. The
shapes of the BRDFs differ on the HAPEX site, which is not surprising since the correlation coefficients of the
regression are rather low in this case (Figure 4.1). The enhancement in the backscattering region is however
present in boths BRDFs. There is also a severe intercalibration problem in this case, which has been thoroughly
studied, by making intercomparisons between AVHRR, airborne POLDER, airborne ASAS, and a TM simulator.
The results of this intercomparison have not permitted, however, to clarify the origin of the problem.
Note that Roujean’s model provides slightly better agreements than Walthall’s for the Alpilles site, the HAPEX
site, and the perpendicular plane of the BOREAS site. They are equivalent for the principal plane of the
BOREAS site.
One sees from Figures 4.4-4.6 that the BRDF reconstruction process with AVHRR data gives a rough estimate of
the BRDF, apparently sufficient to remove the anisotropy effect of the time series of reflectances and indices
(Figures 4.1-4.3). When directional effects are strong and when the across-track plane of AVHRR is close to the
principal plane (case of BOREAS), the BRDF reconstruction is accurate. Note that while the BRDF retrieval is
rather efficient, the retrieval of k1 and k2 separately is not. Figure 4.7 shows the time series of k0, k1 and k2 for
the BOREAS site with either Roujean’s or Li’s model : the k1 and k2 time series are noisy but balance each other
in some way.
4.2.4 Output variables
We have tested two different output variables. One is spectral hemispherical reflectances, obtained by integration
of the BRDF model upon all viewing directions. The other is a bidirectional reflectance, with a nadir viewing and
a sun angle of 45°, also extrapolated from the BRDF model under consideration. Figures 4.8 to 4.10 show for
each of the 3 sites, and for either Roujean’s or Walthall’s model, time series of hemispherical reflectances and
0°-45° bidirectional reflectances, as well as NDVIs constructed with these output variables. Both variables seem
to produce reasonable, noisefree time series. Note the curious peak of NDVI (hemispherical reflectances)
obtained on the Alpilles site in April with Roujean’s model, which does not seem realistic (to be confirmed).
These results do not permit to discriminate the two variables.
We tend to choose the hemispherical reflectances, based on the consideration that this choice has been adopted
by other satellite sensors, such as POLDER (Leroy et al., 1997), MODIS (Wanner et al., 1997), or MERIS
(Weiss et al., 1998). Note that these hemispherical reflectances are evaluated for the average sun zenith angle of
the observations, which varies both with time and with latitude. The 0°-45° bidirectional reflectance has the
advantage of being intercomparable at any time and space. On the other hand the extrapolation needed to get this
value may be severe in some cases and may possibly induce large errors.
4.2.4 Discussion
The above analysis shows that removal of anisotropic effects is indeed feasible using typically one month of
data. Smooth temporal profiles are obtained. In fact, the method basically reduces noise due to directional effects
but at the same time implies a loss of temporal resolution on the profiles, since there is some kind of averaging on
30 days. In many situations, when there are no sharp variations of vegetation (case of Alpilles and BOREAS for
example), this loss is not a problem. But in situations of rapid increase or decrease (case of HAPEX or deciduous
forests for example), the method will not permit to localize precisely in time the vegetation changes.
From this consideration, we propose an alternative method in order to remove directional effects at the same time
as keeping the time resolution. This alternative is to keep the full time resolution of the data (that is, keep all
cloud screened data instead of providing a data every 10 days), extract from 30-day series only the shape of the
BRDF (and not its magnitude), and correct the full resolution data using the shape of the BRDF.
The algorithm would then consist of analyzing 30 days of data, fit a model of the form
(s , v , )  k 0  k1 f 1 (s , v , )  k 2 f 2 (s , v , ) ,
49
Figure 4.4 : comparison of output values for the HAPEX site. Right : hemispherical reflectances and derived NDVI ; left :
directional reflectances for nadir viewing and a sun at 45°, for two models, Roujean (top) and Walthall (bottom).
50
Figure 4.5 : same as Figure 4.4, for the BOREAS site.
51
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Figure 4.6 : same as Figure 4.4, for the Alpilles site.
52
and extract the shape of the BRDF, that is, k1/k0, and k2/k0. Then, we consider a particular day of data, say j,
with reflectance j. This reflectance may be written
 j  k 0 j  k1 j f 1 j  k 2 j f 2 j ,
or

 j  k 0 j 1 


k1
k2
f1j 
f2j 
k0
k0

,
where f1j and f2j are functions of the geometric angles of the observation j. Since j is known, one deduces k0j,
and from this (and the ratios k1/k0 and k2/k0), one deduces the hemispherical reflectance associated to day j.
This alternative is further discussed in Section 5 and will be further investigated in the post-launch phase.
4.3 Perturbating effects
4.3.1 Temporal variabilities of surface reflectances
The algorithm assumes no temporal variability of pixel reflectances during the whole compositing period
(typically 1 month). This is of course partly true, since growth and decay of vegetation can take place in periods
of time smaller than this period, and also because some noise-like time fluctuations of surface reflectance can
occur, due for example to rain or strong wind events, or due to inaccuracies in the atmospheric correction
procedure. These phenomena are studied with multitemporal airborne POLDER data acquired during HAPEX
92, BOREAS 94.
Figures 4.11 to 4.13 display, for the BOREAS experiment, the reflectances in the principal plane of the entire
data set acquired by the airborne POLDER (at the exception of the Fen data) to emphasize the dependences of
the data set upon time and species (Bicheron et al., 1997). Figures 4.11 and 4.12 regroup data per wavelength
(670 nm and 864 nm) and per intensive field period (IFC-1: May-June 1994 and IFC-2 : July-August 1994).
Figure 4.13 compares data at two different periods, for the only data having similar sun zenith angles in the two
periods, that is on the Old Black Spruce and Old Jack Pine sites.
These figures show that diurnal variations of reflectance seem to be much higher than the seasonal variations of
reflectance between IFC-1 and IFC-2. However we interpret the diurnal changes as consequences of changes of
the sun position only. For example, the Old Aspen reflectance changes very much within just 5 days (see top of
Figures 4.12 and 4.13), quite probably simply because the sun angle varies from 39° to 50° between the two data
sets. In an other example (bottom of Figures 4.11 and 4.12), the Old Jack Pine reflectance changes significantly
between July 21 and July 24, with corresponding variations of the sun angle of only 35° to 42°. By contrast,
Figure 4.13 shows that when the sun angle remains nearly constant, the reflectance of conifer stands changes only
slightly from late Spring to early Summer. This is not surprising since the seasonal variations of biophysical
parameters of such covers, especially leaf area index, are very weak.
For the HAPEX-Sahel experiment, Figure 4.14 shows the surface bidirectional reflectance of shrub fallow
observed close to the principal plane at 670 nm and 850 nm on August 24, September 17, and October 4
(Roujean et al., 1996). This figure shows that the reflectances are very much alike for September 17 and October
4, which are characterized by nearly the same sun zenith angle (about 53°). The signatures are much larger for
August 24, characterized by a smaller sun angle (about 34°). It is unlikely that the differences come from the
differences in sun zenith angle, since in general the reflectances increase with the sun angle, and not the opposite.
The differences are rather interpreted as differences in vegetation coverage between August 24 and September
17. The soil being very bright both in the visible and near infrared, the presence of vegetation tends to lower the
signal. Anyway there is evidence that when the surface does not evolve very much, and for similar sun-view
geometries, the signatures of reflectances are very much alike and reproducible. These results will have to be
confirmed with other data from HAPEX-Sahel and from the Alpilles experiment.
53
Figure 4.7 : Time series of k0, k1, k2 for the Old Black Spruce site of BOREAS, with either Roujean’s or Li’s model.
54
Figure 4.8 : comparison of output values for the HAPEX site. Right : hemispherical reflectances and derived NDVI; left :
directional reflectances for nadir viewing and a sun at 45°, for two models, Roujean (top) and Walthall (bottom).
55
Figure 4.9 : same as Figure 4.8, for the BOREAS site.
56
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Figure 4.10 : same as Figure 4.8, for the Alpilles site.
57
Figure 4.11 : Reflectances at 670 nm in principal plane
for BOREAS : Old Jack Pine ( -- ), Old Aspen ( _ ),
Young Jack Pine ( -. ), Old Black Spruce ( ... ) of IFC-1
(top) and IFC-2 (bottom), corrected for atmospheric
effects.
Figure 4.12 : Same as in Figure 5.1 but at 864 nm.
58
Figure 4.13 : Comparison of principal planes acquired in IFC-1 and IFC-2 for Old Black Spruce and Old Jack Pine, at 670
nm (top) and 864 nm (bottom).
59
Figure 4.14 : Shrub fallow directional reflectance for an azimuth close to the principal plane, as estimated from POLDER
measurements in the HAPEX-Sahel experiment on August 24 (full line) with (s=34.7°, =12°), on September 17 (dotted
line) with (s=52.5°, =20°), and on October 4 (dashed line) with (s=54.8°, =5°). Positive (negative) v correspond to
forward (backward) direction. (a) is for 670 nm, whereas (b) is for 850 nm.
60
4.3.2 Coupling of surface and atmosphere effects in BRDF retrieval
The coupling of surface and atmosphere effects in BRDF retrieval is an additional source of error in the
atmospheric correction. In fact, standard atmospheric corrections assume a Lambertian surface : this assumes that
all downward photons from diffuse sky radiation are reflected with the same efficiency R surf irrespective of their
incidence angle. Also, photons eventually reaching the sensor, which have been at least once reflected by the
surface and scattered by the atmosphere, are also reflected with the same efficiency R surf. The error resulting
from this approximation has been evaluated by simulation. The TOA bidirectional reflectance corresponding to
various atmospheric conditions and various surface angular signatures has been computed using the 6S code
(Vermote et al., 1996), which accounts for bidirectional effects of the surface. From the resulting TOA
reflectances, the surface reflectances using the usual correction scheme, and the differences (errors) between
resulting and original surface BRDF have been calculated. Figures 4.15 to 4.17 show examples of original
surface BRDF and associated errors in a few particular cases. The errors increase with aerosol loading and sun
and view zenith angle. They can reach a few 0.01 in typical situations in the near infrared, as shown by Figure
4.16, and typically 0.002-0.006 over vegetated areas in the visible (Figure 4.15). The errors tend to smooth out
the directional signature. They are not negligible and a better atmospheric correction taking into account
explicitly the surface BRDF should be implemented in the processing line.
Vermote et al. (1997) have proposed an algorithm which takes this effect into account. The basic idea is to put
the equation relating the top of atmosphere to the surface reflectance (such as Eq. 1) in a form which makes
appear explicitly the shape of the BRDF (not its actual magnitude). The rationale behind this approach is that
only the shape of the BRDF influences the correction process and not the actual magnitude of the estimated
BRDF. Thus, if one specifies the shape of the BRDF, the obtention of the surface reflectance from the top of
atmosphere reflectance is done by solving a second order equation that only has one positive solution. The details
of the algorithm may be found in Vermote et al. (1997) and in the ATBD (Algorithm Technical description)
document available at http://modarch.gsfc.nasa.gov/MODIS/ATBD/atbd_mod08.pdf .
61
Figure 4.15 : Representation, in polar coordinates, of the surface BRDF (left) and the corresponding error in reflectance units
(right) made when the atmospheric correction is applied assuming a Lambertian surface, at 670 nm, for 3 different surface
types, forest cover (top), grassland (middle), and bare soil (bottom) . The aerosol optical depth is 0.2 at 550 nm; aerosols are
of continental type. The sun zenith angle is 60°. The radius represents viewing zenith angle, the polar angle the relative
azimuth between sun and view directions.
62
Figure 4.16 : same as Figure 4.15 at 850 nm.
63
Figure 4.17 : Errors made, in the same format as in Figures 4.15 and 4.16, when the aerosol optical depth is 0.5, all other
parameters being the same, at 670 nm (left) and 850 nm (right).
64
5. RECOMMENDATIONS FOR VGT
5.1 Atmospheric corrections
Concerning the choice of atmospheric data, the sensitivity studies and error budgets lead us to the following
recommendations:
-Ozone : Ozone absorption occurs mainly in B2 spectral band and is weak. Interannual and daily variabilities are
weak except at high latitudes in winter. Compared to the use of daily data, the use of a climatology leads to
relative errors on the estimation of surface reflectance less than 2%. We recommend the use of a climatology to
correct the TOA reflectance from ozone absorption.
-Water vapor content: For the water vapor content, a study was performed using climatology based on
radiosoundings and analyses of meteorological models at different spatial and temporal resolutions. Climatology
does not reproduce accurately the temporal variability of water vapour content. This leads to relative errors of
5% on the estimation of surface reflectance. So, daily water vapor contents derived from meteorological model
have to be used.
-Aerosols: Absorption and scattering by aerosols affect all spectral bands of VEGETATION, the effects being
more important in the B0 bands than in the MIR band. At the present time, no climatology exists.
As shown in this report, the B0 band opens perspectives of real time correction of the aerosols. This band is quite
sensitive to aerosol content. It is proposed here an algorithm of correction of the aerosols, which uses for any
pixel of land surfaces a time series of typically 30 days of data preceding the day under consideration. Assuming
an aerosol model, and a fixed ratio of the pixel surface reflectances in two bands (say, red and blue) (this ratio
being pixel dependent and unknown a priori), the 30-day time series of data may be processed to retrieve at the
same time the daily aerosol optical depth and the daily surface reflectances in all bands. This algorithm has been
partially validated so far with radiative transfer simulations and with POLDER/ADEOS data on two Sahelian
sites equipped with AERONET sunphotometers. A more complete validation with other sunphotometer sites and
at a scale of continents remains to be done.
As a back-up solution, one may use an aerosol climatology that should be prepared within the next few years by
international teams using (among others) POLDER, MODIS and AERONET data. The use of systems such as
AERONET or POLDER in real time corrections of aerosols is unlikely, because of the inappropriate structure of
their respective processing centers which do not allow, in their present form, real time applications. On the other
hand, these systems may be quite useful for validation purposes.
5.2 Cloud filtering
The temporal filtering/selecting method described in Section 3.5 for LASUR data could be used as a prime
algorithm for cloud detection of VEGETATION data. We have shown that the added value brought by the
threshold tests based on the thermal channels of AVHRR is small in this product.
The temporal filtering proves to be efficient. On the other hand, the envisioned sliding period of 8 to 14 weeks
seems too long for the operational processing in real time that should be applied to VEGETATION data. We
recommend that this period of time should be reduced to typically 4 weeks (1 month), with a simpler temporal
test on the NDVIs, which remains to be prototyped. In addition to NDVI, we recommend to threshold the blue
band (as in POLDER), bright for any type of clouds, and the MIR band, for which liquid-phase clouds have high
reflectances. We also recommend to operate the temporal filtering on the blue band , where spurious data
(uncorrectly cloud screened) show up as spikes on temporal profiles.
A main limitation of the method seems to be the lack of account of directional effects before the
filtering/selecting method is applied. To account for this effect, it is necessary to use in the filtering process a
data base of BRDF, which ideally should depend both on time and space, at the resolutions of VEGETATION.
One may identify three possibilities. One is to use the level 3 BRDF product of POLDER, at space and time
resolutions of 7 km and 10 days. The space resolution may be a source of error for VEGETATION, to be
65
evaluated. Another possibility could be, after, say, one year of VEGETATION orbital life, to construct a BRDF
independent of time but at the space resolution of 1 km, following the method of Ba et al. (1995) described
briefly in Section 4.1. A third possibility could be to use the BRDF evaluated at the previous time step of the
processing line.
5.3 Correction for angular effects
Time series of AVHRR data on three test sites corresponding to the HAPEX, BOREAS and Alpilles experiments
have been considered. The analysis shows that removal of directional effects is indeed feasible by considering
typically 30 days of data, and by the adjustment of a BRDF model on the data. The 30 days duration apperas as a
good compromise to accumulate a sufficient number of cloud free data in most regions of the world. Three
different BRDF models have been tested, used or foreseen to be used on the POLDER, MODIS and MERIS
systems. It turns out that Walthall’s model restitutes the smoothest temporal profiles. On the other hand,
Roujean’s model restitutes the most accurate BRDF, when comparing the reconstructed BRDF from AVHRR
data to the reference BRDFs acquired by the airborne POLDER on these 3 sites. A discussion of the most
relevant output variables has been done, from which we recommend that the algorithmic output should be
spectral hemispherical reflectances. A review of methods foreseen on other sensors show that similar methods are
or will be applied to POLDER/ADEOS, MODIS/EOS, MISR/EOS, and probably to MERIS/ENVISAT and
SEVIRI/MSG.
Based on these results we recommend that a removal of directional effects by considering a 30-day period of data
preceding the day to be processed be implemented in the VEGETATION operational real time processing center.
We noted that replacing the data by a BRDF evaluated on 30 days results in a loss of temporal resolution, which
is of concern for phenology studies. Therefore, it is recommended to keep the full time resolution of
VEGETATION data, and to correct each daily data for directional effects, with a shape of the BRDF determined
with a 30-day period of data.
Such a compositing technique is meaningful only if the time variability of reflectances is sufficiently weak. This
report has shown that no significant time variability has been observed so far, provided the sun-view
configurations are rigourosly the same in successive observations. The atmosphere-surface coupling effect in the
atmospheric correction arises from the fact that the surface is usually assumed Lambertian in the correction.
Simulation studies reported here show that this effect is significant and should be corrected. We propose to use
an algorithm of atmospheric correction taking into account this effect similar to that of Vermote et al. (1997).
5.4 Algorithmic outline
In summary, we propose that the basic improvement of atmospheric corrections and data compositing rely
on the temporal dimension of VEGETATION data. A 30-day temporal depth should be adequate for the
issues of cloud screening, aerosol correction, and removal of directional effects. The full time resolution should
be conserved, and not degraded as in usual compositing techniques.
The proposed algorithmic outline is presented next page.
66
Reflectances TOA
day J to day J-30
External
BRDF
shape
Threshold B0, MIR
Threshold temporal profiles of NDVI, B0
ECMWF
Climat.
O3
External
BRDF
shape
Cloud screened TOA reflectances
day J to day J-30
Flags clear/cloud/snow
Water vapor and ozone correction
Inversion of aerosol optical thickness
Aerosol and molecules correction with account of BRDF shape
Map of
aerosol
models
Surface reflectances
day J to day J-30
Regression with simple BRDF model
Extract the shape of the BRDF
Correct days J to J-30 from the shape of the BRDF
Spectral hemispherical reflectances
day J to day J-30
Shape of the BRDF (model coefficients)
67
6. FUTURE WORK
The work in the post-launch phase will consist in the validation of the proposed algorithms with VGT data. This
validation will be made on selected sites, including the 3 sites studied in this report (Alpilles, HAPEX,
BOREAS), and on some selected sites equipped with sunphotometers in various types of biomes/climates.
We will attempt as much as possible to test also the algorithms on wide portions of the globe.
Our data request follows the above considerations. At the very least, we need daily Top of Atmosphere 1 km
resolution data on the following sites, from April 1, 1998 to November 30, 1998 :
-
Sahel Bidi
HAPEX
France
BOREAS
One tropical zone in Amazonia
The requested size of the sites is 500 x 500 km, except for France for which the size is 1500 x 1500 km. The
France and HAPEX sites are also requested in the STEM investigation lead by G. Dedieu. The sunphotometers
of Lille and Aire-sur-Adour, Sahel Bidi, HAPEX and BOREAS sites will be used in this investigation.
In order to test our algorithms on wide portions of the globe, with a large variety of biomes/climates, we request
the Top of atmosphere 1 km resolution data over the Europe-Africa segment, 30°W-60°E, 0°N-75°N, from April
1 to November 30, 1998. This request obviously includes the above request on the Sahel Bidi, HAPEX and
France sites.
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